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A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression

  • Kevin Hoover
  • Selva Demiralp
  • Stephen J. Perez

    (Department of Economics, University of California Davis)

Graph-theoretic methods of causal search based in the ideas of Pearl (2000), Spirtes, Glymour, and Scheines (2000), and others have been applied by a number of researchers to economic data, particularly by Swanson and Granger (1997) to the problem of finding a data-based contemporaneous causal order for the structural autoregression (SVAR), rather than, as is typically done, assuming a weakly justified Choleski order. Demiralp and Hoover (2003) provided Monte Carlo evidence that such methods were effective, provided that signal strengths were sufficiently high. Unfortunately, in applications to actual data, such Monte Carlo simulations are of limited value, since the causal structure of the true data-generating process is necessarily unknown. In this paper, we present a bootstrap procedure that can be applied to actual data (i.e., without knowledge of the true causal structure). We show with an applied example and a simulation study that the procedure is an effective tool for assessing our confidence in causal orders identified by graph-theoretic search procedures.

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Paper provided by University of California, Davis, Department of Economics in its series Working Papers with number 614.

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Length: 38
Date of creation: 28 Mar 2006
Date of revision:
Handle: RePEc:cda:wpaper:06-14
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